[0001] Embodiments according to the invention relate to an apparatus and a method for determining
a channel quality parameter for a Multiple-Input Multiple-Output (MIMO) communication
system with a number of data streams being simultaneously provided to terminals by
a base station.
[0002] Scheduling in the downlink of a multiuser Multiple-Input Multiple-Output (MIMO) communication
system requires information about the channel qualities, i.e., the Signal-to-Interference-and-Noise
Ratios (SINRs) at the different terminals, for example. One problem for determining
the SINR value at each terminal is that the finally chosen precoder is not known when
it is computed. State-of-the-art methods suggest different approximations of the SINR
value in this case.
[0003] In the downlink 200 (channel) of a multiuser MIMO system as depicted in Fig. 2, the
scheduled and precoded symbols, summarized in the vector
x∼
NC(0M,
Cx) 210, are transmitted via the
M antennas of the base station to the K mobile stations, each equipped with
N receive antennas. Note that the transmit power at the base station is P
Tx = tr(
Cx). With the
kth user's channel matrix
Hk∈
CNxM,
k∈{1,...,
K} 220, and the noise vector
nk∼
NC(0N,
IN) 230 with independent and normal distributed elements of variance one, the perturbed
receive vector 240 of the
kth user can be written as

[0004] Linear precoder and receivers are used and it is assumed that the scheduler has already
selected the users for transmission which are summarized in the set
K ⊆ {1,...,K}. In particular,
dk data symbols of variance
PTx/
D, summarized in the following in the symbol vector
sk ∈
Cdk, have been assigned to user
k. Therefore, the total number of transmitted symbols is
D = Σ
k∈Kdk.
[0005] Then, each symbol vector is precoded using the precoders
Pk∈
CMxdk, and summed up to get the transmit vector

[0006] Here,
P ∈
CMxD is a matrix where the precoders
Pk,
k∈
K, are stacked in a row with ascending
k, in the following denoted as
P=
rstack(
Pk)
k∈K, and
s∈
CD is a vector where the symbol vectors
sk,
k∈
K, are stacked in a column with ascending
k, in the following denoted as
s=
cstack(
sk)
k∈K.
[0007] Next, the receivers at the mobile stations is described. It is assumed that each
user is applying a linear filter
Wk∈
CdkxN to the receive vector
yk to get the estimate

of the
kth user's symbol vector
sk. In particular, the linear Minimum Mean Square Error (MMSE) filter obtained via the
minimization of the mean-square error is considered between
sk and
ŝk=
Wkyk, whose solution computes as (e.g. "
S. Verdú. Multiuser Detection. Cambridge University Press, 1998.")

[0008] Note that it is assumed that each receiver has access to the perfect Channel State
Information (CSI) of its own channel
Hk. However, it has no CSI about the channels of the other users due to the non-cooperative
nature of the multiuser MIMO downlink channel.
[0009] A typical measure for the downlink transmission performance of a multiuser MIMO system
is the sum rate over all users. In the following, it is assumed that maximal one data
stream is assigned to each user, i.e.,
dk ∈ {0,1}. Consequently, the precoding and receiver matrix of each scheduled user
k∈
K,
viz.,
Pk∈
CMxdk and
Wk ∈
CdkxN, shrink to a vector, in the following denoted as
pk∈
CM and

∈
C1xN, respectively. Besides, the number of scheduled users is equal to the number of scheduled
data streams, i.e., |
K| =
D.
[0010] With this assumption, the Signal-to-Interference-and-Noise Ratio (SINR) at the output
of the receive filter

of the
kth user can be written as

and the sum rate computes as

Remember that the variance of each user's symbol is set to
PTx/
D.
[0011] For example, in order to compute the precoder and schedule the users for transmission,
the base station requires information about the channel matrices
Hk for all
k∈{1,...,
K}. This so-called Channel State Information (CSI) is fed back from the terminals to
the base station. Precisely speaking, each user quantizes its channel based on a channel
codebook and feeds back the corresponding codebook index together with an SINR value
which includes a rough estimate of the interference caused by the quantization error
(see e.g. "M. Trivellato, F. Boccardi, and F. Tosato. User selection schemes for MIMO
broadcast channels with limited feedback.
Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th, pages 2089-2093,
April 2007", "Philips. Comparison between MU-MIMO codebook-based channel reporting techniques
for LTE downlink, Oct. 2006. 3GPP TSG RAN WG1 #46 R1-062483", "Philips. Comparison
of MU-MIMO feedback schemes with multiple UE receive antennas, Jan. 2006. 3GPP TSG
RAN WG1 #47bis R1-070346" and "
N. Jindal. A feedback reduction technique for MIMO broadcast channels. Information
Theory, 2006 IEEE International Symposium on, pages 2699-2703, July 2006.") The base station is then computing a precoder, e.g. of type Zero-Forcing (ZF),
based on the quantized CSI and allocates resources using the available SINR information.
Again, since low feedback schemes are of interest, the maximum number of transmitted
data symbols per user may be restricted to one, i.e.,
dk = 1 for all
k∈
K.
[0012] For a channel vector quantization (CVQ) and feedback of channel quality indicator
(CQI) it may be assumed for the first that the precoder
P is known at the mobile receivers such that the MMSE filters

can be computed according to Eq. (4). In order to compute the feedback information,
each user
k quantizes the composite channel vector

being a combination of the linear MMSE filter and the physical channel matrix, by
applying Channel Vector Quantization (CVQ). With the channel codebook
C = {
u1,...,
u2B} where
B denotes the number of necessary bits for indexing the 2
B normalized codebook vectors
uq,
q∈{1,...,2
B}. The quantized composite channel vector
ĝk∈
CM is the codebook entry which has the minimum Euclidean distance to the normalized
composite channel vector, i.e., for
k∈
K: 
[0014] First, one can simply ignore multiuser interference caused by imperfect CSI due to
quantization. The resulting CQI is then an indication of the Signal-to-Noise Ratio
(SNR) defined as

where
and θ
k∈[0,π] denotes the angle between the normalized composite channel vector and the quantized
version thereof.
[0015] Another approximation takes the multiuser interference due to the quantization error
into account:

[0016] Note that all given CQI values are a scaled version of the SINR where the scaling
factor depends on the final number of scheduled data streams (users, terminals) which
is not known to the terminals at this time.
[0018] Then, the approximation of the composite channel vector is obtained by projecting
ĝk back into the row space of
Hk 
and applying the scaling

where

is the right-hand side pseudo-inverse of the matrix
Hk. Applying
gk of Eq. (12) to one of Eqs. (8) or (9) yields the CQI value which is the additional
feedback information to the codebook index ℓ.
[0019] With the quantized composite channel matrix

the ZF precoder at the base station computes as (e.g. "
S. Verdú. Multiuser Detection. Cambridge University Press, 1998.")

with the diagonal matrix Λ
K∈
C|K|x|K| representing power loading. For simulations equal power loading may be assumed where

Recall that |
K| = D due to the fact that d
k = 1 for all
k∈
K.
[0020] With the codebook indices and the CQI values of all users, the base station schedules
the users and computes the ZF precoder as described before. To do so, it calculates
the SINR approximations based on the CQI values, i.e., the scaled versions thereof.
It holds (e.g. "
M. Trivellato, F. Boccardi, and F. Tosato. User selection schemes for MIMO broadcast
channels with limited feedback. Vehicular Technology Conference, 2007. VTC2007-Spring.
IEEE 65th, pages 2089-2093, April 2007" or "Philips. Comparison between MU-MIMO codebook-based channel reporting techniques
for LTE downlink, Oct. 2006. 3GPP TSG RAN WG1 #46 R1-062483").

[0022] Finally, the set
K of scheduled users is used to compute the ZF precoder according to Eqs. (13) and
(14).

[0023] In general, the channel qualities should be known as exactly as possible, while the
feedback information should be kept as low as possible.
[0024] It is the object of the present invention to provide an apparatus for an improved
determination of a channel quality of a channel between a terminal and a base station
of a multiuser multiple-input multiple-output communication system.
[0025] This object is solved by an apparatus according to claim 1 and a method according
to claim 12.
[0026] An embodiment of the invention provides an apparatus for determining a channel quality
parameter for a multiple-input multiple-output communication system with a number
of data streams being simultaneously provided to terminals by a base station. The
channel quality parameter belongs to a channel between one of the terminals and the
base station.
[0027] The apparatus comprises a processor configured to determine the channel quality parameter
based on a number of receive antennas of one of the terminals.
[0028] Embodiments according to the present invention are based on the central idea that
a terminal with a number N of receive antennas may be able to cancel interference
from N-1 interferers. Therefore, the interference between different terminals may
be neglected. So, the channel quality parameter may be determined depending on the
number of receive antennas to improve the determination of the channel quality.
[0029] Some embodiments according to the invention relate to a processor configured to determine
the channel quality parameter based on a first channel quality indicator if the number
of data streams is equal to or lower than the number of receive antennas and based
on a second channel quality indicator if the number of data streams is larger than
the number of receive antennas.
[0030] Some further embodiments according to the invention relate to an apparatus for selecting
terminals to be simultaneously addressed by the base station of the multiple-input
multiple-output communication system.
[0031] Some embodiments according to the invention relate to an apparatus for providing
a channel quality information in a terminal of a multiple-input multiple-output communication
system.
[0032] Embodiments according to the invention will be detailed subsequently referring to
the appended drawings, in which:
- Fig. 1
- is a block diagram of an apparatus for determining a channel quality parameter;
- Fig. 2
- is a schematic illustration of a down link channel of a multiuser multiple-input multiple-output
system;
- Fig. 3
- is a block diagram of an apparatus for selecting terminals to be addressed simultaneously
by a base station;
- Fig. 4
- is a schematic illustration of a feedback process if the second feedback quantity
is transmitted more often than the first feedback quantity;
- Fig. 5
- is a "sum rate vs. signal-to-noise ratio" diagram;
- Fig. 6
- is a "sum rate vs. signal-to-noise ratio" diagram;
- Fig. 7
- is a "sum rate vs. signal-to-noise ratio" diagram;
- Fig. 8
- is a block diagram of an apparatus for providing a channel quality information;
- Fig. 9
- is a flow chart of a method for selecting terminals to be addressed simultaneously
by a base station;
- Fig. 10
- is a flow chart of a method for providing a channel quality information;
- Fig. 11
- is a schematic illustration of a multiuser multiple-input multiple-output system model;
- Fig. 12
- is a schematic illustration of a channel vector quantization;
- Fig. 13a
- is a "sum rate vs. signal-to-noise ratio" diagram;
- Fig. 13b
- is a Table of simulation parameters used for calculating the diagram of Fig. 13a;
- Fig. 14a
- is a "sum rate vs. signal-to-noise ratio" diagram; and
- Fig. 14b
- is a Table of simulation parameters used for calculating the diagram of Fig. 14a.
[0033] Fig. 1 shows a block diagram of an apparatus 100 for determining a channel quality
parameter 112 for a multiple-input multiple-output communication system with a number
of data streams being simultaneously provided to terminals by a base station according
to an embodiment of the invention. The channel quality parameter 112 belongs to a
channel between one of the terminals and the base station.
[0034] The apparatus 100 comprises a processor 110 configured to determine the channel quality
parameter 112 based on a number of receive antennas of one of the terminals.
[0035] Since more than one data stream may be provided simultaneously to terminals by the
base station, wherein one or more data streams may be assigned to one terminal, there
may be interference between the terminals addressed simultaneously. In this case,
the disturbance of a terminal by interference from the other terminals being simultaneously
addressed may strongly depend on the number of simultaneously provided datastreams,
because a terminal may be able to cancel interference from all other terminals addressed
simultaneously if the number of data streams is equal to or less than the number of
receive antennas of this terminal. Therefore, the interference between different terminals
may be neglected if the channel quality parameter is determined depending on the number
of receive antennas. In this way, the determination of the channel quality may be
improved.
[0036] Since a sum rate of all terminals being simultaneously addressed depends on the quality
of the determination of the channel quality, the sum rate may be increased. In other
words, with well-known channel qualities, a sum rate of the downlink of a multiuser
MIMO system may be increased. Increasing the sum rate may enable higher data rates
per terminal and/or more terminals being addressed simultaneously.
[0037] The channel quality parameter 112 may represent the signal-to-interference-and-noise
ratio, the signal-to-noise ratio or another parameter related to the channel quality
of the channel between the base station and a terminal.
[0038] The apparatus 100 may be part of the base station of the multiple-input multiple
output communication system.
[0039] A terminal may, for example, be a mobile phone, a laptop or another communication
device. A terminal may also be called user.
[0040] In some embodiments according to the invention, the channel quality parameter is
determined based on a first channel quality indicator if the number of data streams
is equal or lower than the number of receive antennas of one of the terminals. If
the number of data streams is larger than the number of receive antennas of one of
the terminals, the channel quality parameter is determined based on a second channel
quality indicator.
[0041] Fig. 3 shows a block diagram of an apparatus 300 for selecting terminals to be addressed
simultaneously by a base station of a multiple-input multiple-output communication
system according to an embodiment of the invention. The multiple-input multiple-output
communication system comprises a plurality of terminals.
[0042] The apparatus 300 comprises an apparatus 100 for determining a channel quality parameter
112 as, for example, shown in Fig. 1, connected to a scheduler 310.
[0043] The apparatus 100 determines, for the plurality of terminals, the channel quality
parameter 112 and provides the channel quality parameters 112 to the scheduler 310.
[0044] The scheduler 310 selects the terminals to be simultaneously addressed by the base
station based on the channel quality parameter 112 of each terminal of the terminals
to be simultaneously addressed.
[0045] The apparatus 300 may be part of the base station. Further, the scheduler 310 may
be a part of a processor of the base station.
[0046] In some embodiments according to the invention, the scheduler 310 determines a first
transmission parameter for a first set of selected terminals. A number of data streams
simultaneously provided to the first set of selected terminals is equal to or less
than the number of receive antennas of one of the terminals of the first set of selected
terminals. Additionally the scheduler 310 determines a second transmission parameter
for a second set of selected terminals. A number of data streams simultaneously provided
to the second set of selected terminals is larger than the number of receive antennas
of one of the terminals of the second set of selected terminals. Then the scheduler
310 selects the terminals to be simultaneously addressed by the base station based
on a comparison of the first transmission parameter and the second transmission parameter.
[0047] A transmission parameter may be, for example, a sum rate, a data rate per terminal,
the signal-to-interference-and-noise ratio of all terminals being simultaneously addressed
or another quantity representing the performance of the multiuser downlink.
[0048] It is possible to provide one or more data streams to one selected terminal.
[0049] Although some of the following embodiments are related to one data stream per terminal/user,
it is easy to see, that the described concept can also be used for situations with
more than one data stream per user. In other words, the number of simultaneously provided
data streams may be equal to the number of simultaneously addressed terminals/user.
[0050] In some further embodiments according to the invention, all terminals being simultaneously
addressed by the base station comprises the same number of receive antennas.
[0051] Some embodiments according to the invention relate to terminals using minimum means
square error filtering. A well-known characteristic of MMSE filtering is that it is
the balance between interference cancellation and channel magnitude maximization that
is optimal in an SINR sense (e.g. "
U. Madhow and M.L. Honig. MMSE interference suppression for direct-sequence spreadspectrum
CDMA. Communications, IEEE Transactions on, 42(12):3178-3188, Dec 1994"). For a number of interferers less than or equal to N-1, it may generally be possible
via linear filtering to completely cancel the interference, although the MMSE solution
may leave residual interference at the benefit of an increased SINR. It may be assumed
that with D ≤ N, the full interference cancellation solution exists and is given by

where
ek is the
kth standard basis vector. Due to its SINR maximizing property, it is known that the
SINR under MMSE filtering will be at least as good as the SINR under full interference
cancellation, such that

where the SINR equation (5) is rewritten and the fact is used that

(full interference cancellation). Besides, note that

and
p'k is the
kth column of
P'K. Moreover, due to the zero-forcing beamforming matrix P,

0, where the approximation is only due to imperfect channel knowledge at the transmitter.
In other words, the interferers are already almost canceled from the precoding step,
and full cancellation would only require to move the receive weight vector by a small
amount. Thus it is assumed here that

, where

is a small difference vector. In turn, this allows to make the approximation

such that

[0052] Note that the term in the right of equation (19) may be the SNR approximation of
method T1 from "
Trivellato et al.: User selection schemes for MIMO broadcast channels with limited
feedback. Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th, pages
2089-2093, April 2007". In definitive, it is argued that in a situation where up to
N users (or data streams) are simultaneously scheduled (addressed), the final SINR
could be approximated by the estimated SNR before MMSE filtering, since the MMSE receiver
can efficiently mitigate the residual interference caused by imperfect channel knowledge.
An approximation to the SINR may be proposed which differentiates between the cases
where the number of scheduled users
D (terminals being simultaneously addressed) is equal to or less than
N and when it is larger. For example, the step function

is used as a basis for the evaluation of the SINR of a user k during the feedback
and scheduling process. For the case where
D >
N, the same estimate as scheme T2 from "
Trivellato et al.: User selection schemes for MIMO broadcast channels with limited
feedback. Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th, pages
2089-2093, April 2007" may be kept. As one can see, it is not possible to compute exactly the above without
simultaneous knowledge of
D and at least two other quantities (for example, ∥
gk∥
2 and cos
2 θ
k). Since D is only known by the base station, the SINR estimate can only be computed
there based on user feedback. In the following three feedback and scheduling algorithms
are proposed that use this modified SINR evaluation scheme. The first algorithm R1
requires two quantities to be fed back for CQI (channel quality information, channel
quality parameter) computation. The last two, R2 and R3, require only one, albeit
with a loss in accuracy. The schemes are called
robust since they allow for improved SINR estimation when
D ≤
N. Among other things, this enables the scheduler to limit the number of scheduled
users to
N whenever scheduling more users would result in performance loss.
[0053] In some embodiments according to the invention, two feedback quantities are received
from a terminal by the base station. This may also be called robust scheme R1 or high
feedback mode.
[0054] Because the knowledge of the number of scheduled users
D is required, a direct computation of equation (20) is only possible at the base station.
This first algorithm assumes the mobile (terminal) can feedback two quantities γ
k,1 (first feedback quantity) and γ
k,2 (second feedback quantity) per sub-carrier for the CQI (channel quality parameter,

computation. In such a case, simply

and

. At first glance, it seems that the new CQI could simply be computed at the base
station via

[0055] However, it may be also necessary to consider the scheduling process. As mentioned
before a greedy scheduling scheme (Algorithm 1) is used which incrementally builds
the set of scheduled users (terminals being simultaneously addressed). It turns out
that a mere substitution of the CQI (channel quality parameter) computation step by
equation (21) sometimes results in poorer performance than the original schemes because
of the way the set of scheduled users is built. In particular, in situations where
more than N users are finally scheduled, using the above CQI (channel quality parameter)
results in the first
N users being selected based purely on their SNR approximation. However, when a (
N + 1)th user or more is added to the set, the SINR of the first
N users can sharply decrease since the quality of the interference cancellation during
their selection is not considered. Therefore, it is argued that in such situations,
it is better to build the user set with the assumption that more than
N users will finally be scheduled, and use measure

(second channel quality indicator) at each iteration. However, in the case where
N or less users should finally be scheduled, it may be preferable to use measure

(first channel quality indicator). In practice, this can be achieved by modifying
the scheduling process to include two greedy scheduling runs. Each of the runs is
similar to what was previously described. In the first run, measure

is used and the number of scheduled users is limited to N. Then a second run is started,
where measure

is used instead. Finally, the set of scheduled users chosen is the one between the
two runs that yields the highest estimated transmission parameter, for example the
sum rate. Moreover, in order to promote the scheduling of a large amount of users,
one can define a constant α that artificially reduces the estimated rate of the scheduling
run with a user limit. It was experimentally found that such a constant helps achieving
a higher rate for a certain transmit SNR range.
[0056] The CQI (channel quality parameter,

computation at the base station for scheme R1 may thus be defined as

with the two-run scheduling process previously described. Here, α∈[0,1] can be determined
through some calibration process (for example, α=0.6 is used). As before, the SINR
(for all terminals to be addressed simultaneously) is computed from the CQI (channel
quality parameter) via Eq. (15).
[0057] In some further embodiments according to the invention, only one feedback quantity
is received from a terminal by the base station. This may also be called robust scheme
R2 or low feedback with short-term SNR knowledge.
[0058] As described, the feedback and scheduling scheme R1 requires two scalars to be fed
back to the base station in order to compute the CQI (channel quality parameter) information.
This feedback load may be reduced to one scalar (feedback quantity) only. This may
result in an approximation of the SINR evaluation step function of Eq. (20) since
the knowledge of two quantities, plus the number of scheduled users
D, is required for its exact computation. In this scheme, only one channel magnitude-related
quantity

is fed back to the base station. The channel quality indicators

and

are then approximated from the value of γ
k. This simultaneous knowledge of the approximations of

and

, called

and

, allows the base station to approximate the step function of Eq. (20).

and

may be computed from γ
k with

where δ is a small quantity related to the quantization angle and may be called approximation
parameter. To yield the exact value of

and

one would need to set the approximation parameter δ to sin
2θ
k, but the knowledge of this quantity is not available at the base station. Instead,
one needs to pre-set a value for the approximation parameter δ. A sensible choice
would be, for example, a statistical measure of sin
2θ
k, such as the mean or the median value. In general, channel vector quantization will
yield a relatively small quantization angle. Therefore, it can be argued that the
probability distribution function of sin
2θ
k will be rather tight on the right of zero, and that it may make sense to approximate
sin
2θ
k with a statistical measure.
[0059] The CQI (channel quality parameter) computation formula for scheme R2 may then be
given by

with the scheduling runs and the constant α being defined the same way as in scheme
R1. Since this method assumes the feedback of quantity γ
k, and SNR
k ∝ γ
k(1-δ) where (1-δ) ≈ 1, the base station has a quite accurate knowledge of the users'
SNRs on a short-term basis. In situations where multiuser scheduling with D > N is
not beneficial, this method will barely suffer any performance loss with respect to
method R1, since the SNR is the basis for the scheduling in this situation. However,
some performance degradation may be expected when scheduling with D > N, due to the
lack of information on the instantaneous quantization angle.
[0060] In some embodiments according to the invention, two feedback quantities are received
from a terminal by the base station, wherein one feedback quantity is received more
often than the other. This may also be called robust scheme R3 or low feedback with
long-term SNR knowledge.
[0061] With this method, it is again tried to obtain simultaneous knowledge of both the
SNR indication

(first channel quality indicator) and the SINR estimation

(second quality indicator) at the base station with only one fed back scalar per
user for CQI (channel quality parameter) computation. In method R2, the quantity

was approximated from the value of γ
k which is approximately proportional to the user's SNR. Here, a different approach
is taken by trying to exploit temporal features of the mobile channel to reduce the
feedback load. Recall that

[0062] The squared magnitude of the product

depends on all N x M entries of
Hk. In a typical radio channel, these entries are random variables that follow probabilistic
models (e.g., Rayleigh fading). Moreover, Doppler oscillations will occur with a phase
shift between the subchannels, for example, in case of an OFDM system. Because all
of those subchannels contribute to the value of

a certain "averaging" effect takes place.
Therefore, it is expected that this quantity will not vary as fast in time as the
individual entries of the matrix
Hk. The quantity cos
2θ
k will also be quite stable since θ
k is generally small and thus cos
2θ
k≈1. In other words, one can argue that the SNR of a user with a virtual 1 x M channel
created from a linear combination of its receive antennas has an extended correlation
in time.
[0063] For example, it is assumed that the CQI (channel quality indicator) information is
periodically transmitted from the users (terminals) to the base station with a period
of τ
fb. Each time a feedback report is received, a new scheduling immediately takes place.
The idea of scheme R3 is to use "feedback multiplexing" where either one of two quantities
is transmitted in a given feedback report. It is argued above that the SNR will have
a potentially slow rate of variation. On the other hand, one can expect the quantity

(second channel quality indicator) to vary quickly since it contains a factor of
1/(1+(P
Tx/M)sin
2 θ
k), where θ
k is small and can change abruptly for different channel realizations. The idea is
thus to frequently feedback

(as second feedback quantity) and multiplex an infrequent feedback of a quantity

(as first feedback quantity) which is related to

(first channel quality indicator) and contains an indication of the user's SNR. Let
γ
k[
i] be the quantity that is fed back at time t = iτ
fb, where i ∈ N is a feedback report index. A feedback scheme is proposed where

for all n ∈ N. The value C
1 ∈ N denotes the length of the transmission period (in feedback intervals) of quantity

(first feedback quantity). This feedback process 400 is illustrated in Figure 4.
[0064] The first time line 410 shows the intervals τ
fb 412 at which the base station receives a feedback quantity from a terminal. Fittingly,
the second time line 420 illustrates at which times the second feedback quantity is
received and the third time line 430 shows at which times the first feedback quantity
is received. In this example, the transmission period 432 of the first feedback quantity
is equal to six time intervals 412.
[0065] For the scheduling process, it is proposed to use the same two-runs scheduling as
was described for scheme R1. There, the current value of both quantities

and

was known at scheduling time. However, here, the current value of only one of the
two feedback quantities

or

will be available. At scheduling time, it may be distinguished between two situations:
- 1. Quantity

was just received (i = nC1).
- 2. Quantity

was just received (any other i).
[0066] In Situation 2, basically the same scheduling strategy as in scheme R1 is applied,
with a small modification. In the scheduling run with D ≤ N, the last received value
of the SNR indicator

instead of the instantaneous value of

is used since it is not available. Note that
j=[
i/
C1]
C1. The rest of the scheduling process may be identical to R1.
[0067] In Situation 1, the problem is slightly different. Here, an instantaneous knowledge
of

which is expected to be a fast-varying quantity is not available. In this case, it
is proposed to simply skip the scheduling run that uses this quantity and only perform
the D ≤ N scheduling run with the just received

quantity. Note that situation 1 only occurs once every C
1 feedback report. Therefore, for large C
1, the impact of this skipping becomes negligible.
[0068] The definition of the channel quality estimation

(channel quality parameter) for scheme R3 is summarized in the following equations:

with

as before. The quantity

should be defined next. Note that due to the above scheduling process, this SNR indication
(when fed back at time index i) is used for all scheduling occurring between indices
i and i+C
1-1. In order to have a robust scheme, situations where the outdated SNR indicator
is higher than the current one should be avoided as much as possible. Therefore, it
may be suggested to

i.e., the minimum

over the last C1 period may be transmitted. With such an indicator selection criterion,
it is aimed to minimize the instances where the D ≤ N scheduling run would falsely
yield a higher estimated rate than the unrestricted run. Note that alternatives such
as the time average or an arbitrary percentile of the distribution of

over the last period could also be considered.
[0069] Due to the feedback multiplexing, the base station needs to be aware of which quantity
is fed back by the users. For example, in cases where feedback information is transmitted
upon a request from the base station, one could simply define two different commands
that define which quantity is requested. However, in cases where the users send periodic
feedback without intervention from the BS, for example, either a synchronization mechanism
would need to be implemented or an extra information in the feedback payload would
identify the transmitted quantity. In that case, the former may not increase the feedback
load at the expense of increased implementation complexity and possibly extra synchronization
information. On the other hand, the latter would be feasible at a low implementation
cost, at the expense of a slightly increased feedback information. Note that only
one bit is needed to identify the transmitted quantity. Moreover, this information
bit can simultaneously describe the feedback quantity for all sub-carriers, such that
the overall load in a feedback report that covers multiple sub-carriers may only be
increased by one bit.
[0070] Some embodiments according to the invention relate to simulation results.
[0071] In the following simulations, the aim is to compare the state-of-the-art feedback
and scheduling schemes T1 and T2 from "
M. Trivellato, F. Boccardi, and F. Tosato. User selection schemes for MIMO broadcast
channels with limited feedback. Vehicular Technology Conference, 2007. VTC2007-Spring.
IEEE 65th, pages 2089-2093, April 2007" to the results obtained with the proposed robust schemes R1, R2, and R3. The parameters
used for these simulations are shown in the Table below. In the following, the method
T1-Limit denotes the T1 scheme where the number of maximum data streams D is limited
to the number of receive antennas N, i.e.,
D≤
D=N.
[0073] The first thing one can notice is that this scheme yields superior performance for
the whole transmit SNR range. However, remember that R1 requires two quantities to
be fed back by the users for CQI (channel quality parameter) evaluation. The key feature
of scheme R1 is illustrated here as it is able to perform as well or better than T2
in the low and mid-SNR range, when scheduling more than
N users is beneficial. At high SNR, when limiting the number of users to
N is preferable, algorithm R1 is able to join the performance curve of T1-Limit. It
thus performs a smooth linking between curves T2 and T1-Limit. In fact, for all graphs
presented, R1 is the best scheme for the whole transmit SNR range. These results are
rather encouraging and suggest that SINR estimation based on the step function of
Eq. (20) produces better results overall than any of the schemes seen so far.
[0074] The performance of the robust scheme R2, which only requires one feedback parameter
for CQI evaluation is discussed next. In the case of algorithm R2, one can see that
its performance never falls below T1-Limit. This would be expected since both of these
schemes work with an accurate knowledge of the users' SNRs. Moreover, in situations
where T2 performs better than T1-Limit, algorithm R2 is sometimes able to approach
the performance of T2. This is the case with the DFT codebook and an antenna separation
of 0.5λ
c, as can be seen in, e.g., Figure 6. It shows a "sum rate vs. signal-to-noise ratio"
diagram 600 for zero-forcing (ZF) receiver with channel vector quantization (CVQ)
with different feedback using DFT codebook, wherein the distance between receive antennas
of the terminals

and the distance of the receive antennas of the base station

is equal to

[0075] However, at other times (e.g., DFT codebook with antenna separation of 3.5λ
c), the algorithm is not able to perform much better than T1-Limit. It appears that
R2 is superior to T1-Limit in situations where the sum rate gap between T2 and T1-Limit
is large, and in these simulations it never performs worse than T1-Limit. However,
R2 suffers a performance loss with respect to T2 in situations where T2 is superior
to T1-Limit. This performance loss varies in function of the transmit SNR. In summary,
it would seem that using algorithm R2 in settings where T1-Limit is the best algorithm
of the current schemes does not cause any performance loss, as is the case when, e.g.,
using the random or Grassmannian codebook with B = 4. This is illustrated in Figure
7. It shows a "sum rate vs. signal-to-noise ratio" diagram 700 for zero-forcing receiver
with channel vector quantization with different feedback using a random codebook,
wherein the distance between receive antennas of the terminals

and the distance of the receive antennas of the base station

is equal to 0.5λ
c 
[0076] On the other hand, the algorithm yields acceptable performance when T2 is the best
scheme, such as when the DFT codebook is used with a highly correlated channel. The
R2 scheme needs the approximation parameter δ, also called calibrating parameter.
As this value may change over time, it might be necessary to design a scheme where
the users to regularly update this value and communicate it to the base station.
[0077] Next the results for algorithm R3 are analyzed. In the SNR ranges where T2 is superior
to T1-Limit, one can see that the performance of R3 follows closely the one from T2.
This can be observed, e.g., in Figure 5 between 0 and 15 dB. Indeed, that can be expected
since the scheme R3 is based on the users feeding back the quantity

most of the time. The small difference that remains between the two schemes is due
to the one out of C1 feedback reports where

is not feedback, but rather the SNR indicator

This forces the algorithm to set

to zero in those cases and results in a minor performance loss. In the simulations,
the feedback cycles of the users all begin at the same time instant, which means that
one out of
C1 scheduler decision only have quantities

available. The remaining C
1-1 out of
C1 scheduler decisions have the instantaneous CQI indicator

and outdated indicator

available for all users. Therefore, these C
1 - 1 out of
C1 decisions should produce the same result as T2 when T2 is superior to T1-Limit. One
can see that by using a larger C
1, the minor performance loss in such situations will further decrease.
[0078] Focusing on the ranges where T1-Limit is superior to T2, as can be seen, e.g., on
Figure 5 for transmit SNRs larger than 22 dB, the algorithm R3 allows for the same
rate of increase as T1-Limit, albeit with a constant performance loss (≈ 1.5 dB on
the example figure). Indeed, in the high SNR situations where it is preferable to
limit the users to N, quantity

must be used for the scheduling decisions. This quantity is generally outdated since
it is not frequently transmitted. Therefore, it does not contain an indication of
the instantaneous user SNR but rather an indication of the worse user SNR over the
period before the indicator was transmitted to the base station. As one can expect,
scheduling based on this indicator causes performance degradation with respect to
scheduling based on instantaneous SNR as with T1-Limit.
[0079] In summary, one can conclude that the proposed schemes perform good for all investigated
scenarios whereas the state-of-the-art methods degrade drastically for specific channel
conditions.
[0080] Fig. 8 shows a block diagram of an apparatus 800 for providing a channel quality
information 822 in a terminal of a multiple-input multiple-output communication system
according to an embodiment of the invention. The apparatus 800 comprises a channel
quality determiner 810 and a processor 820.
[0081] The channel quality determiner 810 determines a channel quality 812 of a channel
between the terminal and the base station.
[0082] The channel quality determiner 810 is connected to the processor 820 and provides
the determined channel quality 812 to the processor 820.
[0083] The determined channel quality 812 may correspond to the channel matrix of the channel
between the terminal and the base station.
[0084] The processor 820 determines a value of only one feedback quantity based on the determined
channel quality 812 and provides the feedback quantity as the channel quality information
822. Alternatively, the processor 820 determines values of two feedback quantities
based on the determined channel quality 812 and provides these two feedback quantities
as the channel quality information 822. The first feedback quantity is determined
as often as the second feedback quantity or the second feedback quantity is determined
more often than the first feedback quantity.
[0085] The apparatus 800 may be part of a terminal of the multiple-input multiple-output
communication system.
[0086] Some embodiments according to the invention relate to a method for determining a
channel quality parameter for a multiple-input multiple-output communication system
with a number of data streams being simultaneously provided to terminals by a base
station. The channel quality parameter belongs to a channel between one of the terminals
and the base station.
[0087] The method comprises determining the channel quality parameter based on a number
of receive antennas of one of the terminals.
[0088] Fig. 9 shows a flow chart of a method 900 for selecting terminals to be addressed
simultaneously by a base station of a multiple-input multiple-output communication
system according to an embodiment of the invention. The multiple-input multiple-output
communication system comprises a plurality of terminals.
[0089] The method 900 comprises determining 910 channel quality parameters for the plurality
of terminals and selecting 920 the terminals to be simultaneously addressed.
[0090] The channel quality parameters for the plurality of terminals are determined based
on a number of receive antennas of one of the terminals.
[0091] The terminals to be simultaneously addressed by the base station are selected based
on the channel quality parameter of each terminal of the terminals to be simultaneously
addressed.
[0092] Fig. 10 shows a flow chart of a method 1000 for providing a channel quality information
in a terminal of a multiple-input multiple-output communication system according to
an embodiment of the invention. The method 1000 comprises determining 1010 a channel
quality of a channel between the terminal and the base station and determining 1020
a value of only one feedback quantity or of two feedback quantities.
[0093] Determining 1020 the value of the one feedback quantity is based on the determined
channel quality. This feedback quantity may be provided as the channel quality information.
Alternatively, values of two feedback quantities are determined based on the determined
channel quality and then these two feedback quantities may be provided as the channel
quality information. In this case, the first feedback quantity is determined as often
as the second feedback quantity or the second feedback quantity is determined more
often than the first feedback quantity.
[0094] Some embodiments according to the invention relate to an approximation of a signal-to-interference-and-noise
ratio. The quality of the approximation depends strongly on the number of finally
scheduled data streams (selected terminals to be addressed simultaneously), which
is not taken into account by state of the art methods. The presented concept may improve
the performance of the scheduler by exploiting this dependency and adapting the feedback
information accordingly.
[0095] Some further embodiments according to the invention relate to a feedback method with
compressed channel quality indication. This may be implemented in a multiuser multiple-input
multiple-output (MIMO) system model 1100, for example, shown in Fig. 11. It shows
a plurality of terminals 1110 with terminals 1120 being simultaneously addressed by
a base station 1130.
[0096] A mitigation of multiuser interference may be done by linear precoding, for example,
Zero-Forcing (ZF) at the transmitter/eNB (enhanced Node B) and scheduling, for example,
with a greedy algorithm aiming at high sum rate. This may require a Channel State
Information (CSI) at the transmitter/eNB (at the base station). A channel state information
may be represented by a Channel Direction Information (CDI) via Channel Vector Quantization
(CVQ) and a Channel Quality Information (CQI), for example, a Signal-to-Interference-and-Noise
ratio (SINR).
[0097] The quantization of a channel-receiver chain vector
gk =
HkTwk (the composite channel vector) may be done by choosing a codebook entry with minimum
Euclidean distance to
gk. A problem is that the channel-receive chain vector is not known at the quantization
step, because it depends on the precoder (used by the base station). One possibility
may be to estimate the composite channel vector by choosing the closest vector (minimum
Euclidean distance) in the range space of the transpose channel matrix.
[0098] Fittingly, Fig. 12 shows a schematic illustration of a channel vector quantization
1200. It shows a quantized composite channel vector 1210 with the closest codebook
entry to the range space 1230 of transpose channel matrix as a channel direction indication.
Further, it shows an estimated composite channel vector 1220 needed for the channel
quality information computation. The range space 1230 of the transpose channel matrix
comprises all possible composite channel vectors.
[0099] One possible channel quality indication (CQI) is to estimate the signal-to-interference-and-noise
ratio.

[0100] In this case, the numerator relates to an estimated signal power, the 1 of the denominator
relates to a noise power and the rest of the denominator relates to an estimated interference
power due to a quantization error. For calculating the signal-to-interference-and-noise
ratio, two quantities may be fed back to the base station.
[0101] By using:

the estimated signal interference noise ratio may be written as:

[0102] In this case, the feedback of only one quantity may be necessary.
[0103] Without the interference term, the signal interference noise ratio may be written
as:

[0104] Some embodiments according to the invention relate to an estimation of the signal-to-interference-and-noise
ratio (SINR) when a minimum means square error (MMSE) receiver is used. A problem
for the SINR estimation is that the

finally used is different from one assumed for a channel vector quantization and

finally used is not known at feedback time.
[0105] Particularly, when the number of transmitted data streams (the number of terminals)
is smaller than the number of receive (RX) antennas, the minimum mean square error
receiver can mitigate all interferers. Therefore, the SINR estimation should depend
on the number of transmitted data streams, but this information is not known at the
receiver.
[0106] Therefore, a feedback of compressed channel quality information may be done. In this
way, the signal-to-interference-and-noise ratio may be calculated as follows:

[0107] This must be computed at the transmitter/eNB (at the base station), because the number
of terminals D is only known at the base station. One possibility for the calculation
is that two quantities are fed back by a terminal, which was described above as scheme
R1. Another possibility is to feed back only one quantity (which means only one quantity
per time interval), which was mentioned above as schemes R2 and R3. Using scheme R2,
only one quantity is transmitted, for example, ∥
gk∥
2 cos
2θ
k and another one is "guessed", for example sin
2θ
k. By using scheme R3, a quantity for the case D>N is transmitted most of the time
and instead occasionally a measure of, for example, ∥
gk∥
2 cos
2θ
k over time is transmitted.
[0108] For example, Figs. 13a and 13b show the system performance, with DFT codebook and
low correlation. Especially for high signal-to-noise ratios, the described robust
schemes R1, R2, R3 result in a higher sum rate than, for example, the schemes introduced
in "
M. Trivellato, F. Boccardi, and F. Tosato: "User selection schemes or MIMO broadcast
channels with limited feedback", Vehicular Technology Conference, 2007. VTC2007-Spring,
IEEE 65th, pages 2089-2093, April 2007" as shown in the diagram 1300A of Fig. 13a. Fittingly, Fig. 13b shows a Table 1300B
of the simulation parameters used for calculating the diagram 1300A of Fig. 13a.
[0109] Further, Figs. 14a and 14b show the system performance when using a random codebook
with high correlation. Once again, especially for high signal-to-noise ratio, the
robust schemes R1, R2, R3 show a better performance in comparison to the schemes introduced
in "
M. Trivellato, F. Boccardi, and F. Tosato: "User selection schemes or MIMO broadcast
channels with limited feedback", Vehicular Technology Conference, 2007. VTC2007-Spring,
IEEE 65th, pages 2089-2093, April 2007" as shown in the diagram 1400A of Fig. 14a. Fig. 14b shows a Table 1400B of the simulation
parameters used for calculating the diagram 1400A of Fig. 14a.
[0110] In some embodiments according to the invention, the described approach increases
the sum rate or cell throughput compared to state-of-the-art techniques with the same
or less amount of feedback. A higher cell throughput in this case means higher data
rates and/or more users per cell (per base station).
[0111] Alternatively to increasing the cell throughput, the described approach can be used
to decrease transmit power resulting in less costs for power consumption at the base
station and/or less electromagnetic radiation.
[0112] The proposed schemes exploit the available feedback amount for the channel quality
information in a more sophisticated way. This may result in higher cell throughputs
compared to state-of-the-art techniques, for example, at high signal-to-noise ratio
values.
[0113] In the present application, the same reference numerals are partly used for objects
and functional units having the same or similar functional properties.
[0114] In particular, it is pointed out that, depending on the conditions, the inventive
scheme may also be implemented in software. The implementation may be on a digital
storage medium, particularly a floppy disk or a CD with electronically readable control
signals capable of cooperating with a programmable computer system so that the corresponding
method is executed. In general, the invention thus also consists in a computer program
product with a program code stored on a machine-readable carrier for performing the
inventive method, when the computer program product is executed on a computer. Stated
in other words, the invention may thus also be realized as a computer program with
a program code for performing the method, when the computer program product is executed
on a computer.